Pricing and listing configuration recommendation engine

ABSTRACT

In an example embodiment, an item characteristic is received, the item characteristic pertaining to an item being listed for sale, by a seller, via an ecommerce service. Then, a plurality of past transactions of items having the item characteristic are analyzed. Based on this analysis, a first set of one or more optimal listing configuration parameters are identified in accordance with a first set of listing criteria. Then, the first set of one or more identified optimal listing configuration parameters to the seller in a user interface that permits the seller to change one or more listing configuration parameters based on the presentation.

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent files or records, but otherwise reserves all copyrightrights whatsoever. The following notice applies to the software and dataas described below and in the drawings that form a part of thisdocument: Copyright eBay, Inc. 2013, All Rights Reserved.

TECHNICAL FIELD

The present application relates generally to electronic commerce and, inone specific example, to a pricing and listing configurationrecommendation engine.

BACKGROUND

Ecommerce transactions, such as online sales and auctions, have nowsurpassed traditional consumer transactions in total revenue. Typicallyecommerce transactions begin with a seller creating a listing, whichincludes an offer to sell or auction the item. The listing is createdusing one or more configuration parameters. For example, in the case ofan online auction, a seller may be able to specify reserve price,starting bid, time of auction end, number of photos attached to listing,whether free shipping is offered, promotional placement, and otherconfiguration parameters.

BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments are illustrated by way of example and not limitation inthe figures of the accompanying drawings in which:

FIG. 1 is a network diagram depicting a client-server system, withinwhich one example embodiment may be deployed.

FIG. 2 is a block diagram illustrating marketplace and paymentapplications and that, in one example embodiment, are provided as partof application server(s) 118 in the networked system.

FIG. 3 is a block diagram illustrating a system of auction listingrecommendation in accordance with an example embodiment.

FIG. 4 is a block diagram illustrating a relevancy system in moredetail, in accordance with an example embodiment.

FIG. 5 is a flow diagram illustrating a method of making arecommendation for an ecommerce item listing in accordance with anexample embodiment.

FIG. 6 is a screen capture illustrating the user interface as a user(seller) is ready to enter listing configuration parameters inaccordance with an example embodiment.

FIG. 7 is a screen capture illustrating the user interface with apreliminary recommendation option in accordance with an exampleembodiment.

FIG. 8 is a screen capture illustrating the user interface with theseller having selected the “sell it quick” button in accordance with anexample embodiment.

FIG. 9 is a screen capture illustrating the user interface with theseller having selected the “get highest price” button.

FIG. 10 is a flow diagram illustrating a method of providing optimallisting configuration parameters in an ecommerce listing in accordancewith an example embodiment.

FIG. 11 is a block diagram illustrating a mobile device, according to anexample embodiment.

FIG. 12 is a block diagram of machine in the example form of a computersystem within which instructions, for causing the machine to perform anyone or more of the methodologies discussed herein, may be executed.

DETAILED DESCRIPTION

Example methods and systems for text translation in ecommerce servicesare provided. It will be evident, however, to one skilled in the artthat the present inventive subject matter may be practiced without thesespecific details.

According to various exemplary embodiments, historical usage and salesinformation regarding ecommerce transactions is used to makerecommendations to sellers as to one or more configuration settings. Inthis way, sellers can improve their changes of obtaining high salesvolumes and/or maximizing sales prices.

FIG. 1 is a network diagram depicting a client-server system 100, withinwhich one example embodiment may be deployed. A networked system 102, inthe example forms of a network-based marketplace or publication system,provides server-side functionality, via a network 104 (e.g., theInternet or a Wide Area Network (WAN)), to one or more clients. FIG. 1illustrates, for example, a web client 106 (e.g., a browser, such as theInternet Explorer browser developed by Microsoft Corporation of Redmond,Wash. State) and a programmatic client 108 executing on respectivedevices 110 and 112.

An Application Program Interface (API) server 114 and a web server 116are coupled to, and provide programmatic and web interfaces respectivelyto, one or more application servers 118. The application servers 118host one or more marketplace applications 120 and payment applications122. The application servers 118 are, in turn, shown to be coupled toone or more database servers 124 that facilitate access to one or moredatabases 126.

The marketplace applications 120 may provide a number of marketplacefunctions and services to users who access the networked system 102. Thepayment applications 122 may likewise provide a number of paymentservices and functions to users. The payment applications 122 may allowusers to accumulate value (e.g., in a commercial currency, such as theU.S. dollar, or a proprietary currency, such as “points”) in accounts,and then later to redeem the accumulated value for products (e.g., goodsor services) that are made available via the marketplace applications120. While the marketplace and payment applications 120 and 122 areshown in FIG. 1 to both form part of the networked system 102, it willbe appreciated that, in alternative embodiments, the paymentapplications 122 may form part of a payment service that is separate anddistinct from the networked system 102.

Further, while the system 100 shown in FIG. 1 employs a client-serverarchitecture, the embodiments are, of course, not limited to such anarchitecture, and could equally well find application in a distributed,or peer-to-peer, architecture system, for example. The variousmarketplace and payment applications 120 and 122 could also beimplemented as standalone software programs, which do not necessarilyhave networking capabilities.

The web client 106 accesses the various marketplace and paymentapplications 120 and 122 via the web interface supported by the webserver 116. Similarly, the programmatic client 108 accesses the variousservices and functions provided by the marketplace and paymentapplications 120 and 122 via the programmatic interface provided by theAPI server 114. The programmatic client 108 may, for example, be aseller application (e.g., the TurboLister application developed by eBayInc., of San Jose, Calif.) to enable sellers to author and managelistings on the networked system 102 in an off-line manner, and toperform batch-mode communications between the programmatic client 108and the networked system 102.

FIG. 1 also illustrates a third party application 128, executing on athird party server machine 130, as having programmatic access to thenetworked system 102 via the programmatic interface provided by the APIserver 114. For example, the third party application 128 may, utilizinginformation retrieved from the networked system 102, support one or morefeatures or functions on a website hosted by the third party. The thirdparty website may, for example, provide one or more promotional,marketplace, or payment functions that are supported by the relevantapplications of the networked system 102.

FIG. 2 is a block diagram illustrating marketplace and paymentapplications 120 and 122 that, in one example embodiment, are providedas part of application server(s) 118 in the networked system 102. Theapplications 120 and 122 may be hosted on dedicated or shared servermachines (not shown) that are communicatively coupled to enablecommunications between server machines. The applications 120 and 122themselves are communicatively coupled (e.g., via appropriateinterfaces) to each other and to various data sources, so as to allowinformation to be passed between the applications 120 and 122 or so asto allow the applications 120 and 122 to share and access common data.The applications 120 and 122 may furthermore access one or moredatabases 126 via the database servers 124.

The networked system 102 may provide a number of publishing, listing,and price-setting mechanisms whereby a seller may list (or publishinformation concerning) goods or services for sale, a buyer can expressinterest in or indicate a desire to purchase such goods or services, anda price can be set for a transaction pertaining to the goods orservices. To this end, the marketplace and payment applications 120 and122 are shown to include at least one publication application 200 andone or more auction applications 202, which support auction-formatlisting and price setting mechanisms (e.g., English, Dutch, Vickrey,Chinese, Double, Reverse auctions, etc.). The various auctionapplications 202 may also provide a number of features in support ofsuch auction-format listings, such as a reserve price feature whereby aseller may specify a reserve price in connection with a listing and aproxy-bidding feature whereby a bidder may invoke automated proxybidding.

A number of fixed-price applications 204 support fixed-price listingformats (e.g., the traditional classified advertisement-type listing ora catalogue listing) and buyout-type listings. Specifically, buyout-typelistings (e.g., including the Buy-It-Now (BIN) technology developed byeBay Inc., of San Jose, Calif.) may be offered in conjunction withauction-format listings, and allow a buyer to purchase goods orservices, which are also being offered for sale via an auction, for afixed-price that is typically higher than the starting price of theauction.

Store applications 206 allow a seller to group listings within a“virtual” store, which may be branded and otherwise personalized by andfor the seller. Such a virtual store may also offer promotions,incentives, and features that are specific and personalized to arelevant seller.

Reputation applications 208 allow users who transact, utilizing thenetworked system 102, to establish, build, and maintain reputations,which may be made available and published to potential trading partners.Consider that where, for example, the networked system 102 supportsperson-to-person trading, users may otherwise have no history or otherreference information whereby the trustworthiness and credibility ofpotential trading partners may be assessed. The reputation applications208 allow a user (for example, through feedback provided by othertransaction partners) to establish a reputation within the networkedsystem 102 over time. Other potential trading partners may thenreference such a reputation for the purposes of assessing credibilityand trustworthiness.

Personalization applications 210 allow users of the networked system 102to personalize various aspects of their interactions with the networkedsystem 102. For example a user may, utilizing an appropriatepersonalization application 210, create a personalized reference page atwhich information regarding transactions to which the user is (or hasbeen) a party may be viewed. Further, a personalization application 210may enable a user to personalize listings and other aspects of theirinteractions with the networked system 102 and other parties.

The networked system 102 may support a number of marketplaces that arecustomized, for example, for specific geographic regions. A version ofthe networked system 102 may be customized for the United Kingdom,whereas another version of the networked system 102 may be customizedfor the United States. Each of these versions may operate as anindependent marketplace or may be customized (or internationalized)presentations of a common underlying marketplace. The networked system102 may accordingly include a number of internationalizationapplications 212 that customize information (and/or the presentation ofinformation by the networked system 102) according to predeterminedcriteria (e.g., geographic, demographic or marketplace criteria). Forexample, the internationalization applications 212 may be used tosupport the customization of information for a number of regionalwebsites that are operated by the networked system 102 and that areaccessible via respective web servers 116.

Navigation of the networked system 102 may be facilitated by one or morenavigation applications 214. For example, a search application (as anexample of a navigation application 214) may enable key word searches oflistings published via the networked system 102. A browse applicationmay allow users to browse various category, catalogue, or inventory datastructures according to which listings may be classified within thenetworked system 102. Various other navigation applications 214 may beprovided to supplement the search and browsing applications.

In order to make listings available via the networked system 102 asvisually informing and attractive as possible, the applications 120 and122 may include one or more imaging applications 216, which users mayutilize to upload images for inclusion within listings. An imagingapplication 216 also operates to incorporate images within viewedlistings. The imaging applications 216 may also support one or morepromotional features, such as image galleries that are presented topotential buyers. For example, sellers may pay an additional fee to havean image included within a gallery of images for promoted items.

Listing creation applications 218 allow sellers to conveniently authorlistings pertaining to goods or services that they wish to transact viathe networked system 102, and listing management applications 220 allowsellers to manage such listings. Specifically, where a particular sellerhas authored and/or published a large number of listings, the managementof such listings may present a challenge. The listing managementapplications 220 provide a number of features (e.g., auto-relisting,inventory level monitors, etc.) to assist the seller in managing suchlistings. One or more post-listing management applications 222 alsoassist sellers with a number of activities that typically occurpost-listing. For example, upon completion of an auction facilitated byone or more auction applications 202, a seller may wish to leavefeedback regarding a particular buyer. To this end, a post-listingmanagement application 222 may provide an interface to one or morereputation applications 208, so as to allow the seller conveniently toprovide feedback regarding multiple buyers to the reputationapplications 208.

Dispute resolution applications 224 provide mechanisms whereby disputesarising between transacting parties may be resolved. For example, thedispute resolution applications 224 may provide guided procedureswhereby the parties are guided through a number of steps in an attemptto settle a dispute. In the event that the dispute cannot be settled viathe guided procedures, the dispute may be escalated to a third partymediator or arbitrator.

A number of fraud prevention applications 226 implement fraud detectionand prevention mechanisms to reduce the occurrence of fraud within thenetworked system 102.

Messaging applications 228 are responsible for the generation anddelivery of messages to users of the networked system 102 (such as, forexample, messages advising users regarding the status of listings at thenetworked system 102 (e.g., providing “outbid” notices to bidders duringan auction process or to provide promotional and merchandisinginformation to users)). Respective messaging applications 228 mayutilize any one of a number of message delivery networks and platformsto deliver messages to users. For example, messaging applications 228may deliver electronic mail (e-mail), instant message (IM), ShortMessage Service (SMS), text, facsimile, or voice (e.g., Voice over IP(VoIP)) messages via the wired (e.g., the Internet), plain old telephoneservice (POTS), or wireless (e.g., mobile, cellular, WiFi, WiMAX)networks 104.

Merchandising applications 230 support various merchandising functionsthat are made available to sellers to enable sellers to increase salesvia the networked system 102. The merchandising applications 230 alsooperate the various merchandising features that may be invoked bysellers, and may monitor and track the success of merchandisingstrategies employed by sellers.

The networked system 102 itself, or one or more parties that transactvia the networked system 102, may operate loyalty programs that aresupported by one or more loyalty/promotions applications 232. Forexample, a buyer may earn loyalty or promotion points for eachtransaction established and/or concluded with a particular seller, andbe offered a reward for which accumulated loyalty points can beredeemed.

FIG. 3 is a block diagram illustrating a system 300 of auction listingrecommendation in accordance with an example embodiment. The system 300may include a bid database 302, a won auction database 304, a sellerdatabase 306, a customer database 308, a listing details database 310,and an auction details database 312. It should be noted that while thesedatabases 302-312 are depicted as distinct databases, in some exampleembodiments they are all contained in a single physical or logicaldatabase.

The bid database 302 may contain information about bids placed onauctions, including, for example, listing identification, customeridentification, timestamp, and amount. The won auction database 304 maycontain information about auctions that have been won, including, forexample, customer identification, listing identification, selleridentification, amount, and timestamp.

The seller database 306 may contain information about sellers,including, for example, seller identification, seller reputation, sellerhistorical transactions, seller location, etc. The customer database 308may contain information about customers, including, for example,customer identification, customer reputation, customer historicaltransactions, customer location, etc.

The listing details database 310 may contain details about particularlistings, including, for example, seller identification, listingidentification, listing price, listing type, auction versus fixed price,starting price, buy it now enabled, end date, scheduled end date, finalprice, shipping details, etc.

The auction details database 312 may contain general information aboutpast auction details, such as, for example, seller identification,listing identification, customer identification, and bid identification.

It should be noted that the term “Auction” as used herein shall beinterpreted broadly to encompass listings where the buyer may close thetransaction by making a single bid, also known as a buy-it-now price.Additionally, the term should also be interpreted broadly to coverfixed-price auctions, where a set price is provided for goods andmultiple bids are not accepted (e.g., buyers simply purchase or notpurchase the item at a set price).

A relevancy system 314 may model the propensity to close for aparticular auction based on historical listing details, historicalbidding activity, bidding density based on various parameters (e.g.,starting price, buy it now enabled), closing price based on variousparameters (e.g., starting price) and estimate time to close. Therelevancy system may create a multi-variate regression model to use onfuture listings. The relevancy system 314 may access various submodules316-322 to aid in performing this modeling.

A closing price submodule 316 may estimate closing price based onauctions and bid activity on those listings in similar subcategories, oron specific products when that information is available (such as throughUniversal Product Code (UPC) or Stock Keeping Unit (SKU) codes).

A bid value submodule 318 may estimate the propensity to attract bids,and what bids will be, based on historical bid logs for listings insimilar subcategories, or on specific products when that information isavailable.

A will close submodule 320 may estimate the propensity that the auctionwill sell based on past auctions and bid activity in those auctions insimilar subcategories, or on specific products when that information isavailable.

A time to close submodule 322 may estimate the time to close based onpast auctions and bid activity in those auctions in similarsubcategories, or on specific products when that information isavailable.

The relevancy system 314 may output one or more recommendations to alisting portal 324, which may display the one or more recommendationswhen a seller is creating a listing.

An ecommerce listing may be any offer for sale of a product or serviceon an ecommerce web site or through an ecommerce service. This mayinclude, for example, an auction listing, or a “buy it now” listing, butalso could include a more traditional product sale page such as a webpage devoted to a product sold by a particular retailer through theretailer's web page or service.

As an example, if a new smartphone sells for an average price of $150across many auctions, and auctions that end between 2 AM and 6 AM ESTtend to underperform the average by 20%, while auctions that end between8 PM and 11 PM EST tend to outperform the average by 20%, then thesystem can recommend that sellers configure their listings of thissmartphone to end between 8 PM and 11 PM EST for an estimate $30increase in sale price.

As another example, if a new smartphone sells for an average price of$150 across many auctions, but those with a promotional listingoutperform the average by 20% and only cost the seller $5 extra, thenthe net benefit to the seller to purchase that promotional listing is$25. The system can recommend that particular promotional listingproduct to the seller, an estimate its value.

It should be noted that the recommendations may not be provided just tosellers, but also may be made to potential buyers as well. For example,buyers can be alerted to specific listings that are priced below theaverage. This information may be presented in a number of differentways, including email or text alerts, or an icon or notificationpresented next to a listing on a screen of listings, the icon ornotification saying “under market price” or “good deal” or the like.

While a number of different listing configuration parameters that thesystem can use to make recommendations have been described above, one ofordinary skill in the art will recognize that there are other factorsthat may be used. The following is intended to be a non-exhaustive listof potential listing configuration parameters:

1. Product Condition

2. Time of Auction Expiration

3. Presence of Photo

4. Number of Photos

5. Quality of Photos

6. Reserve Price Set

7. Reserve Price Amount

8. Shipping Price

9. Assigned Category

10. Buy It Now Available

11. Buy It Now Differential to Starting Bid

12. Starting Bid Price

13. Location of Seller

14. Seller Reputation

15. Seller Historical Transactions

16. Promotional Listing Selected

These configuration parameters can be used to estimate impact to anumber of different auction variables, such as sales price, number ofbids, time to sale, and probability of sale.

In an example embodiment, item title, condition, category, aspects,seller identification and seller segment (e.g., consumer to consumer orbusiness to consumer) are utilized to make recommendations based onhistorical similar items that were sold on in the ecommerce service.FIG. 4 is a block diagram illustrating a relevancy system, such asrelevancy system 314 from FIG. 3, in more detail, in accordance with anexample embodiment. A similar items (“simitems”) client 400 may take aninput item and query a search engine to find items that are similar,ranked by, for example, title similarity. The simitems client 400 mayinclude a simitems query builder 402, which may build a query based onsuch information as item title, condition, category, and seller segment,and a simitems query executor 404, which may take the built query anduse it to query a search engine, such as a search engine tied to theecommerce service.

In an example embodiment, the query itself can be tuned via, forexample, one or more tuning knobs presented in a user interface toadministrators, which allows an administrator to vary the tightness ofthe query.

A seller guidance simitems client 406 may oversee the various aspects ofthe relevancy system, including receiving a request from a requestvalidator 408 and passing the request, which includes an identificationof an item, to the simitems client 400.

The retrieved items from the result of the query being sent to thesearch engine may be passed to the simitem outlier filtering 410. Byreducing outlier similar items, the system is able to increase theprecision of price guidance. The simitem outlier filtering 410 may be amodule that is trained over time and maximize the precision of priceguidance.

The simitem outlier filtering 410 may include simitem feature extraction412, which may extract one or more features of the retrieved searchengine results. The simitem outlier filtering 410 may also include asimitem classifier 414, which may predict whether the items returnedfrom the search engine query are similar enough to the item in theoriginal request, for the purpose of price calculation. In an exampleembodiment, the simitem classifier 414 may use a support vector machine(SVM) classifier. For feature sets, it may use title tokens, textsimilarity, category id, condition match, item cost, total cost (withshipping), watch count, bidder count, total views, and rank. The modelmay be trained offline based on completed items that sold. In an exampleembodiment, the model may be hosted in a storage service, with a cacheto periodically refresh/load the model in memory. As such, any updatesto the model can be automatically picked up by the service, via asimitem classifier loader 416.

While not pictured, at this point a price corrector may be use tocorrect for any price differentiation due to known factors, such asshipping cost. There are three main categories of items based onshipping: flat rate, free shipping, and calculated shipping. Typicallythe price of the free shipping items are slightly inflated to cover forthe shipping costs to the seller. The price corrector can estimate whatthis inflation is and correct for it. Specifically, by looking atsimilar items with flat rate shipping, the system can compute what thepercentage f the item cost is the typical shipping cost. The median suchcost can be subtracted from the free shipping items cost to negate theinflation.

Price guidance 416 is then able to provide a price suggestion based onthe results from the simitem classifier 414. Any number of differentmechanisms can be used to derive the price. In an example embodiment,the median price of the top N similar items, as determined by thesimitem classifier 414, is used.

Format guidance 418 may also be provided. One or more format metrics ofsimilar items, perhaps the top N similar items as determined by thesimitem classifier 414, may be use to recommend alterations of similarmetrics in the seller listing.

In an example embodiment, price rounding may also be implemented. Theresult of price guidance based solely on a computed mean or median ofthe top N similar items may take the form a of any valid real numbers(for example $13.78). In an example embodiment, these values may berounded to a more psychologically acceptable value (for example, $13.99)that potentially results in greater adoption of the recommendation bysellers. The rounded price may have the flexibility to maximize overmultiple metrics, such as conversion, average selling price, andpopularity/frequency. After selecting the metric to optimize, the systemmay round within a certain dollar or cents threshold based on theoriginal price. This allows an administrator to configured howaggressive he or she wants to be when rounding versus maximizing forprices for the given metric. The system may also only examinesignificant pricing points to remove noise.

As an example, a price recommendation service may return a figure of$24.57 and an administrator wishes to maximize conversion. Using thisprice, the system can look up the maximum and minimum range to round to.Since it is about $20 and less than $50, the threshold may be set to $1,so the system looks at all significant pricing points between $23.57 and$25.57. The system may find that $23.95, $23.99, $24, $24.50, $24.95,$24.99, $25, and $25.50 are significant points and out of this range thesystem looks up the conversion percentages for each price point andchooses the price with the maximum conversion.

FIG. 5 is a flow diagram illustrating an example method 500 of making arecommendation for an ecommerce item listing in accordance with anexample embodiment. In an example embodiment, the method 500 may beperformed by the relevancy system 314 of FIG. 3

At operation 502, a search query may be performed on the ecommerceservice to obtain similar items that were specifically sold, using oneor more of the factors/parameters described above to influence orpopulate the query. This may be performed using any of a number ofdifferent possible flavors for the query with varying quorum thresholds.Examples include high inverse document frequency (IDF) and kand.

At operation 504, a similar item classifier may be used to filter outoutliers that may skew the prediction. At operation 506, pricecorrection may be applied to account for an inflated price on an itemoffering free shipping. At operation 508, the result set may bereordered with a different sort key and sort order and the top N itemsmay be taken to form a price and format compute set. N may be defined bya price compute size. The sort key may be, for example, score, view itemcount watch count, time to sell, total cost, etc).

At operation 510, the mean or median sold price of the compute set isdetermined. At operation 512, a majority voting technique is applied tothe compute set to return the dominant format. If there is no clearmajority, the system may revert to a configured default.

At operation 514, in order to determine a recommended price, the top Nauction items are taken and the media or mean start price isrecommended.

FIGS. 6-9 are screen captures illustrating a user interface providing anecommerce listing recommendation in accordance with an exampleembodiment. FIG. 6 is a screen capture illustrating the user interface600 as a user (seller) is ready to enter listing configurationparameters in accordance with an example embodiment. The parametersinclude one or more photos 602, listing details 604 such as title anddescription, pricing details 606 such as list as, but it now, duration,scheduled start time, starting price, and shipping, and otherpreferences 608 such as whether returns are permitted.

FIG. 7 is a screen capture illustrating the user interface 600 with apreliminary recommendation option in accordance with an exampleembodiment. Here, the seller is presented with a “let us help you list”button 702. Pressing this button 702 may provide general recommendationsfor one or more of the listing parameters 602, 604, 606, 608.Additionally, more specific recommendations may be provided if theseller selects a “sell it quick” button 704 or a “get highest price”button 706. The “sell it quick” button 704 acts to providerecommendations geared towards ensuring that a product sells as quicklyas possible. The “get highest price” button 706 acts to providerecommendations geared towards ensuring that a product sells for as higha price as possible. The seller is then able to choose what is moreimportant to him or her: price or speed.

FIG. 8 is a screen capture illustrating the user interface 600 with theseller having selected the “sell it quick” button 704 in accordance withan example embodiment. In response to this action, a summary window 800is displayed indicating an estimated sale price and time to sell withthe configuration parameters specified. The various configurationparameters 604, 606, 608 have been completed. In an example embodiment,the system may fill out one or more of these parameters 604, 606, 608 onbehalf of the user. In another example embodiment, these parameters 604,606, 608 have been filled out by the seller and the seller uses theinformation in the summary window 800 to adjust one or more of theparameters 404, 406, 408.

Notably, here the system has estimated the sale price at $108-$118 andthe estimated time to sell as 2 days.

In an example embodiment, the information in the summary window 800 maybe dynamically updated as the seller changes one or more of theconfiguration parameters 602, 604, 606, 608. Thus, for example, if theuser changes one of the pricing details 606, such as duration, thesystem may perform an analysis of estimated sale price and time to sellbased on the new duration and update these values immediately in thesummary window 800.

Additionally, a specific recommendation window 802 is provided, hererecommending that the user include at least 2 high resolution photos,based on the recommendations from the system.

FIG. 9 is a screen capture illustrating the user interface 600 with theseller having selected the “get highest price” button 706. In responseto this action, one or more of the configuration parameters 604, 606,608 have changed from FIG. 8, which in turn has caused the informationin the summary window 800 to change as well. Now the estimated saleprice is $115-$124, which is higher than in FIG. 8, but the estimatedtime to sell is 6 days, which is longer than in FIG. 8.

Additionally, the specific recommendation window 802 has been updated toprovide a recommendation in line with the desire to get the highestprice. Here, the specific recommendation window 802 is recommending morephotos than in FIG. 8.

FIG. 10 is a flow diagram illustrating a method 1000 of providingoptimal listing configuration parameters in an ecommerce listing inaccordance with an example embodiment. At operation 1002, an itemcharacteristic is received, the item characteristic pertaining to anitem being listed for sale, by a seller, via an ecommerce service. Atoperation 1004, a plurality of past transactions of items having theitem characteristic are analyzed. At operation 1006, based on theanalysis of the plurality of past transactions, a first set of one ormore optimal listing configuration parameters is identified inaccordance with a first set of listing criteria. At operation 1008, thefirst set of one or more identified optimal listing configurationparameters is presented to the seller in a user interface that permitsthe seller to change one or more listing configuration parameters basedon the presentation.

Example Mobile Device

FIG. 11 is a block diagram illustrating a mobile device 1100, accordingto an example embodiment. The mobile device 1000 may include a processor1102. The processor 1102 may be any of a variety of different types ofcommercially available processors 1102 suitable for mobile devices 1100(for example, an XScale architecture microprocessor, a microprocessorwithout interlocked pipeline stages (MIPS) architecture processor, oranother type of processor 1102). A memory 1104, such as a random accessmemory (RAM), a flash memory, or other type of memory, is typicallyaccessible to the processor 1102. The memory 1104 may be adapted tostore an operating system (OS) 1106, as well as application programs1108, such as a mobile location enabled application that may provideLBSs to a user. The processor 1102 may be coupled, either directly orvia appropriate intermediary hardware, to a display 11010 and to one ormore input/output (I/O) devices 1112, such as a keypad, a touch panelsensor, a microphone, and the like. Similarly, in some embodiments, theprocessor 1102 may be coupled to a transceiver 1114 that interfaces withan antenna 1116. The transceiver 1114 may be configured to both transmitand receive cellular network signals, wireless data signals, or othertypes of signals via the antenna 1116, depending on the nature of themobile device 1100. Further, in some configurations, a GPS receiver 1118may also make use of the antenna 1116 to receive GPS signals.

Modules, Components and Logic

Certain embodiments are described herein as including logic or a numberof components, modules, or mechanisms. Modules may constitute eithersoftware modules (e.g., code embodied (1) on a non-transitorymachine-readable medium or (2) in a transmission signal) orhardware-implemented modules. A hardware-implemented module is atangible unit capable of performing certain operations and may beconfigured or arranged in a certain manner. In example embodiments, oneor more computer systems (e.g., a standalone, client or server computersystem) or one or more processors 1102 may be configured by software(e.g., an application or application portion) as a hardware-implementedmodule that operates to perform certain operations as described herein.

In various embodiments, a hardware-implemented module may be implementedmechanically or electronically. For example, a hardware-implementedmodule may comprise dedicated circuitry or logic that is permanentlyconfigured (e.g., as a special-purpose processor, such as a fieldprogrammable gate array (FPGA) or an application-specific integratedcircuit (ASIC)) to perform certain operations. A hardware-implementedmodule may also comprise programmable logic or circuitry (e.g., asencompassed within a general-purpose processor 1102 or otherprogrammable processor 1102) that is temporarily configured by softwareto perform certain operations. It will be appreciated that the decisionto implement a hardware-implemented module mechanically, in dedicatedand permanently configured circuitry, or in temporarily configuredcircuitry (e.g., configured by software) may be driven by cost and timeconsiderations.

Accordingly, the term “hardware-implemented module” should be understoodto encompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired) or temporarily ortransitorily configured (e.g., programmed) to operate in a certainmanner and/or to perform certain operations described herein.Considering embodiments in which hardware-implemented modules aretemporarily configured (e.g., programmed), each of thehardware-implemented modules need not be configured or instantiated atany one instance in time. For example, where the hardware-implementedmodules comprise a general-purpose processor 1102 configured usingsoftware, the general-purpose processor 1102 may be configured asrespective different hardware-implemented modules at different times.Software may accordingly configure a processor 1102, for example, toconstitute a particular hardware-implemented module at one instance oftime and to constitute a different hardware-implemented module at adifferent instance of time.

Hardware-implemented modules can provide information to, and receiveinformation from, other hardware-implemented modules. Accordingly, thedescribed hardware-implemented modules may be regarded as beingcommunicatively coupled. Where multiple of such hardware-implementedmodules exist contemporaneously, communications may be achieved throughsignal transmission (e.g., over appropriate circuits and buses thatconnect the hardware-implemented modules). In embodiments in whichmultiple hardware-implemented modules are configured or instantiated atdifferent times, communications between such hardware-implementedmodules may be achieved, for example, through the storage and retrievalof information in memory structures to which the multiplehardware-implemented modules have access. For example, onehardware-implemented module may perform an operation, and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware-implemented module may then,at a later time, access the memory device to retrieve and process thestored output. Hardware-implemented modules may also initiatecommunications with input or output devices, and can operate on aresource (e.g., a collection of information).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors 1102 that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors 1102 may constitute processor-implementedmodules that operate to perform one or more operations or functions. Themodules referred to herein may, in some example embodiments, compriseprocessor-implemented modules.

Similarly, the methods described herein may be at least partiallyprocessor-implemented. For example, at least some of the operations of amethod may be performed by one or processors 1102 orprocessor-implemented modules. The performance of certain of theoperations may be distributed among the one or more processors 1102, notonly residing within a single machine, but deployed across a number ofmachines. In some example embodiments, the processor 1102 or processors1102 may be located in a single location (e.g., within a homeenvironment, an office environment or as a server farm), while in otherembodiments the processors 1102 may be distributed across a number oflocations.

The one or more processors 1102 may also operate to support performanceof the relevant operations in a “cloud computing” environment or as a“software as a service” (SaaS). For example, at least some of theoperations may be performed by a group of computers (as examples ofmachines including processors), these operations being accessible via anetwork (e.g., the Internet) and via one or more appropriate interfaces(e.g., application program interfaces (APIs).)

Electronic Apparatus and System

Example embodiments may be implemented in digital electronic circuitry,or in computer hardware, firmware, software, or in combinations of them.Example embodiments may be implemented using a computer program product,e.g., a computer program tangibly embodied in an information carrier,e.g., in a machine-readable medium for execution by, or to control theoperation of, data processing apparatus, e.g., a programmable processor1102, a computer, or multiple computers.

A computer program can be written in any form of programming language,including compiled or interpreted languages, and it can be deployed inany form, including as a stand-alone program or as a module, subroutine,or other unit suitable for use in a computing environment. A computerprogram can be deployed to be executed on one computer or on multiplecomputers at one site or distributed across multiple sites andinterconnected by a communication network.

In example embodiments, operations may be performed by one or moreprogrammable processors 1102 executing a computer program to performfunctions by operating on input data and generating output. Methodoperations can also be performed by, and apparatus of exampleembodiments may be implemented as, special purpose logic circuitry,e.g., a field programmable gate array (FPGA) or an application-specificintegrated circuit (ASIC).

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. Inembodiments deploying a programmable computing system, it will beappreciated that that both hardware and software architectures requireconsideration. Specifically, it will be appreciated that the choice ofwhether to implement certain functionality in permanently configuredhardware (e.g., an ASIC), in temporarily configured hardware (e.g., acombination of software and a programmable processor 1102), or acombination of permanently and temporarily configured hardware may be adesign choice. Below are set out hardware (e.g., machine) and softwarearchitectures that may be deployed, in various example embodiments.

Example Machine Architecture and Machine-Readable Medium

FIG. 12 is a block diagram of machine in the example form of a computersystem 1200 within which instructions, for causing the machine toperform any one or more of the methodologies discussed herein, may beexecuted. In alternative embodiments, the machine operates as astandalone device or may be connected (e.g., networked) to othermachines. In a networked deployment, the machine may operate in thecapacity of a server or a client machine in server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine may be a personal computer (PC), atablet PC, a set-top box (STB), a personal digital assistant (PDA), acellular telephone, a web appliance, a network router, switch or bridge,or any machine capable of executing instructions (sequential orotherwise) that specify actions to be taken by that machine. Further,while only a single machine is illustrated, the term “machine” shallalso be taken to include any collection of machines that individually orjointly execute a set (or multiple sets) of instructions to perform anyone or more of the methodologies discussed herein.

The example computer system 1200 includes a processor 1202 (e.g., acentral processing unit (CPU), a graphics processing unit (GPU) orboth), a main memory 1204 and a static memory 1206, which communicatewith each other via a bus 1208. The computer system 1200 may furtherinclude a video display unit 1210 (e.g., a liquid crystal display (LCD)or a cathode ray tube (CRT)). The computer system 1200 also includes analphanumeric input device 1212 (e.g., a keyboard or a touch-sensitivedisplay screen), a user interface (UI) navigation device 1214 (e.g., amouse), a disk drive unit 1216, a signal generation device 1218 (e.g., aspeaker) and a network interface device 1220.

Machine-Readable Medium

The disk drive unit 1216 includes a machine-readable medium 1222 onwhich is stored one or more sets of instructions and data structures(e.g., software) 1224 embodying or utilized by any one or more of themethodologies or functions described herein. The instructions 1224 mayalso reside, completely or at least partially, within the main memory1204 and/or within the processor 1202 during execution thereof by thecomputer system 1200, the main memory 1204 and the processor 1202 alsoconstituting machine-readable media 1222.

While the machine-readable medium 1222 is shown in an example embodimentto be a single medium, the term “machine-readable medium” may include asingle medium or multiple media (e.g., a centralized or distributeddatabase, and/or associated caches and servers) that store the one ormore instructions 1224 or data structures. The term “machine-readablemedium” shall also be taken to include any tangible medium that iscapable of storing, encoding or carrying instructions 1224 for executionby the machine and that cause the machine to perform any one or more ofthe methodologies of the presentdisclosure or that is capable ofstoring, encoding or carrying data structures utilized by or associatedwith such instructions 1224. The term “machine-readable medium” shallaccordingly be taken to include, but not be limited to, solid-statememories, and optical and magnetic media. Specific examples ofmachine-readable media 1222 include non-volatile memory, including byway of example semiconductor memory devices, e.g., erasable programmableread-only memory (EPROM), electrically erasable programmable read-onlymemory (EEPROM), and flash memory devices; magnetic disks such asinternal hard disks and removable disks; magneto-optical disks; andCD-ROM and DVD-ROM disks.

Transmission Medium

The instructions 1224 may further be transmitted or received over acommunications network 1226 using a transmission medium. Theinstructions 1224 may be transmitted using the network interface device1220 and any one of a number of well-known transfer protocols (e.g.,HTTP). Examples of communication networks include a local area network(“LAN”), a wide area network (“WAN”), the Internet, mobile telephonenetworks, plain old telephone (POTS) networks, and wireless datanetworks (e.g., WiFi and WiMax networks). The term “transmission medium”shall be taken to include any intangible medium that is capable ofstoring, encoding or carrying instructions 1224 for execution by themachine, and includes digital or analog communications signals or otherintangible media to facilitate communication of such software.

Although an embodiment has been described with reference to specificexample embodiments, it will be evident that various modifications andchanges may be made to these embodiments without departing from thebroader spirit and scope of the disclosure. Accordingly, thespecification and drawings are to be regarded in an illustrative ratherthan a restrictive sense. The accompanying drawings that form a parthereof, show by way of illustration, and not of limitation, specificembodiments in which the subject matter may be practiced. Theembodiments illustrated are described in sufficient detail to enablethose skilled in the art to practice the teachings disclosed herein.Other embodiments may be utilized and derived therefrom, such thatstructural and logical substitutions and changes may be made withoutdeparting from the scope of this disclosure. This Detailed Description,therefore, is not to be taken in a limiting sense, and the scope ofvarious embodiments is defined only by the appended claims, along withthe full range of equivalents to which such claims are entitled.

Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent to those of skill in theart upon reviewing the above description.

What is claimed is:
 1. A system comprising: a memory storing a databaseof past transactions in an ecommerce service; a relevancy systemexecutable by a processor and configured to: receive an itemcharacteristic, the item characteristic pertaining to an item beinglisted for sale, by a seller, via an ecommerce service; analyze aplurality of past transactions of items having the item characteristic;based on the analysis of the plurality of past transactions, identify afirst set of one or more optimal listing configuration parameters inaccordance with a first set of listing criteria; and a listing portalconfigured to present the first set of one or more identified optimallisting configuration parameters to the seller in a user interface thatpermits the seller to change one or more listing configurationparameters based on the presentation.
 2. The system of claim 1, whereinthe relevancy system includes: a client configured to generate a queryto a search engine for similar items based on a request identifying theitem being listed for sale; and an outlier filtering module configuredto filter outliers from the plurality of transactions of items havingthe item characteristic.
 3. The system of claim 1, wherein the listingportal is a web page executing in a web browser.
 4. The system of claim1, wherein the listing portal is located on a user machine and therelevancy system is located on a server.
 5. The system of claim 4,wherein the user machine is a mobile device.
 6. A method comprising:receiving an item characteristic, the item characteristic pertaining toan item being listed for sale, by a seller, via an ecommerce service;analyzing a plurality of past transactions of items having the itemcharacteristic; based on the analysis of the plurality of pasttransactions, identifying a first set of one or more optimal listingconfiguration parameters in accordance with a first set of listingcriteria; and presenting the first set of one or more identified optimallisting configuration parameters to the seller in a user interface thatpermits the seller to change one or more listing configurationparameters based on the presentation.
 7. The method of claim 6, whereinthe item characteristic is an identification of a product.
 8. The methodof claim 6, wherein the item characteristic is an item category.
 9. Themethod of claim 6, wherein the first set of listing criteria ismaximizing sale price.
 10. The method of claim 6, wherein the first setof listing criteria is minimizing time until sale.
 11. The method ofclaim 6, wherein the identifying the first set of one or more optimallisting configuration parameters is performed in response to anindication from the seller that the first set of listing criteria shouldbe used.
 12. The method of claim 11, further comprising: in response toan indication from the seller that a second set of listing criteriashould be used: based on the analysis of the plurality of pasttransactions, identifying a second set of one or more optimal listingconfiguration parameters based on a second set of listing criteria; andpresenting the second set of one or more identified optimal listingconfiguration parameters to the seller in the user interface.
 13. Themethod of claim 6, wherein the user interface is part of a userinterface the seller uses to list the item for sale.
 14. The method ofclaim 6, wherein the user interface is a web page.
 15. A non-transitorymachine-readable storage medium having embodied thereon instructionsexecutable by one or more machines to perform operations comprising:receiving an item characteristic, the item characteristic pertaining toan item being listed for sale, by a seller, via an ecommerce service;analyzing a plurality of past transactions of items having the itemcharacteristic; based on the analysis of the plurality of pasttransactions, identifying a first set of one or more optimal listingconfiguration parameters in accordance with a first set of listingcriteria; and presenting the first set of one or more identified optimallisting configuration parameters to the seller in a user interface thatpermits the seller to change one or more listing configurationparameters based on the presentation.
 16. The non-transitory machinereadable storage medium of claim 15, wherein the item characteristic isan identification of a product.
 17. The non-transitory machine readablestorage medium of claim 15, wherein the item characteristic is an itemcategory.
 18. The non-transitory machine readable storage medium ofclaim 15, wherein the first set of listing criteria is maximizing saleprice.
 19. The non-transitory machine readable storage medium of claim15, wherein the first set of listing criteria is minimizing time untilsale.
 20. The non-transitory machine readable storage medium of claim15, wherein the identifying the first set of one or more optimal listingconfiguration parameters is performed in response to an indication fromthe seller that the first set of listing criteria should be used.